LAENet: Light-weight asymmetric encoder-decoder network for semantic segmentation
Encode-decoder structure is used in deep learning for real-time dense segmentation task. On account of the limitation of calculation burden on mobile devices, we present a light-weight asymmetric encoder-decoder network in this paper, namely LAENet, which quickly and efficiently accomplish the task...
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| Vydáno v: | Journal of physics. Conference series Ročník 1966; číslo 1; s. 12047 - 12053 |
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01.07.2021
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| ISSN: | 1742-6588, 1742-6596 |
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| Abstract | Encode-decoder structure is used in deep learning for real-time dense segmentation task. On account of the limitation of calculation burden on mobile devices, we present a light-weight asymmetric encoder-decoder network in this paper, namely LAENet, which quickly and efficiently accomplish the task of real-time semantic segmentation. We employ an asymmetric convolution and group convolution structure combined with dilated convolution and dense connectivity to reduce computation cost and model size, which can guarantee adequate receptive field and enhance the model learning ability in encoder. On the other hand, feature pyramid networks (FPN) structure combine attention mechanism and ECRE block are utilized in the decoder to strike a balance between the network complexity and segmentation performance. Our approach achieves only have 0.84M parameters, and is able to reach 66 FPS in a single GTX 1080Ti GPU. Experiments on Cityscapes datasets demonstrate that superior performance of LAENet is better than the existing segmentation network, in terms of speed and accuracy trade-off without any post-processing. |
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| AbstractList | Encode-decoder structure is used in deep learning for real-time dense segmentation task. On account of the limitation of calculation burden on mobile devices, we present a light-weight asymmetric encoder-decoder network in this paper, namely LAENet, which quickly and efficiently accomplish the task of real-time semantic segmentation. We employ an asymmetric convolution and group convolution structure combined with dilated convolution and dense connectivity to reduce computation cost and model size, which can guarantee adequate receptive field and enhance the model learning ability in encoder. On the other hand, feature pyramid networks (FPN) structure combine attention mechanism and ECRE block are utilized in the decoder to strike a balance between the network complexity and segmentation performance. Our approach achieves only have 0.84M parameters, and is able to reach 66 FPS in a single GTX 1080Ti GPU. Experiments on Cityscapes datasets demonstrate that superior performance of LAENet is better than the existing segmentation network, in terms of speed and accuracy trade-off without any post-processing. |
| Author | Hong, Liangyi Pan, Yongbin Duan, Shukai Wang, Lidan |
| Author_xml | – sequence: 1 givenname: Liangyi surname: Hong fullname: Hong, Liangyi organization: Chongqing Brain Science Collaborative Innovation Center , China – sequence: 2 givenname: Shukai surname: Duan fullname: Duan, Shukai organization: Chongqing Brain Science Collaborative Innovation Center , China – sequence: 3 givenname: Lidan surname: Wang fullname: Wang, Lidan organization: Chongqing Brain Science Collaborative Innovation Center , China – sequence: 4 givenname: Yongbin surname: Pan fullname: Pan, Yongbin organization: Chongqing Brain Science Collaborative Innovation Center , China |
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| Cites_doi | 10.1109/TIP.2020.3042065 10.1109/TPAMI.2017.2699184 10.3390/app10031166 10.1109/5.726791 10.1109/TPAMI.2016.2644615 |
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| DOI | 10.1088/1742-6596/1966/1/012047 |
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| References_xml | – start-page: 552 year: 2018 ident: JPCS_1966_1_012047bib13 – volume: 30 start-page: 1169 year: 2020 ident: JPCS_1966_1_012047bib15 article-title: Cgnet: A light-weight context guided network for semantic segmentation publication-title: IEEE Transactions on Image Processing doi: 10.1109/TIP.2020.3042065 – start-page: 234 year: 2015 ident: JPCS_1966_1_012047bib16 – volume: 40 start-page: 834 year: 2017 ident: JPCS_1966_1_012047bib5 article-title: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs publication-title: IEEE transactions on pattern analysis and machine intelligence doi: 10.1109/TPAMI.2017.2699184 – start-page: 6848 year: 2018 ident: JPCS_1966_1_012047bib14 – year: 2017 ident: JPCS_1966_1_012047bib12 publication-title: Mobilenets: Efficient convolutional neural networks for mobile vision applications – start-page: 770 year: 2016 ident: JPCS_1966_1_012047bib4 – volume: 41 start-page: 1302 year: 2020 ident: JPCS_1966_1_012047bib7 article-title: Image semantic segmentation based on convolutional neural network publication-title: Journal of Chinese Computer Systems – start-page: 2881 year: 2017 ident: JPCS_1966_1_012047bib17 – volume: 25 start-page: 1097 year: 2012 ident: JPCS_1966_1_012047bib2 article-title: Imagenet classification with deep convolutional neural networks publication-title: Advances in neural information processing systems – volume: 10 start-page: 1166 year: 2020 ident: JPCS_1966_1_012047bib6 article-title: A novel digital modulation recognition algorithm based on deep convolutional neural network publication-title: Applied Sciences doi: 10.3390/app10031166 – start-page: 269 year: 2018 ident: JPCS_1966_1_012047bib10 – start-page: 405 year: 2018 ident: JPCS_1966_1_012047bib8 – start-page: 1860 year: 2019 ident: JPCS_1966_1_012047bib19 – start-page: 1 year: 2019 ident: JPCS_1966_1_012047bib20 – volume: 86 start-page: 2278 year: 1998 ident: JPCS_1966_1_012047bib1 article-title: Gradient-based learning applied to document recognition publication-title: Proceedings of the IEEE doi: 10.1109/5.726791 – year: 2014 ident: JPCS_1966_1_012047bib3 publication-title: Very deep convolutional networks for large-scale image recognition – start-page: 3213 year: 2016 ident: JPCS_1966_1_012047bib11 – year: 2015 ident: JPCS_1966_1_012047bib21 publication-title: Multi-scale context aggregation by dilated convolutions – year: 2016 ident: JPCS_1966_1_012047bib9 publication-title: Enet: A deep neural network architecture for real-time semantic segmentation – volume: 39 start-page: 2481 year: 2017 ident: JPCS_1966_1_012047bib18 article-title: Segnet: A deep convolutional encoder-decoder architecture for image segmentation publication-title: IEEE transactions on pattern analysis and machine intelligence doi: 10.1109/TPAMI.2016.2644615 |
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| SubjectTerms | Asymmetry Coders Convolution Deep learning Electronic devices Encoders-Decoders Post-production processing Real time Semantic segmentation Semantics Weight reduction |
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| Title | LAENet: Light-weight asymmetric encoder-decoder network for semantic segmentation |
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